• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.427-435
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• 2012

The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.117-124
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• 2013

This study examined that how precisely MFCC, LPC, energy, and pitch related parameters of the speech data, which have been used mainly for voice recognition system could predict the vocal emotion categories as well as dimensions of vocal emotion. 110 college students participated in this experiment. For more realistic emotional response, we used well defined emotion-inducing stimuli. This study analyzed the relationship between the parameters of MFCC, LPC, energy, and pitch of the speech data and four emotional dimensions (valence, arousal, intensity, and potency). Because dimensional approach is more useful for realistic emotion classification. It results in the best vocal cue parameters for predicting each of dimensions by stepwise multiple regression analysis. Emotion categorizing accuracy analyzed by LDA is 62.7%, and four dimension regression models are statistically significant, p<.001. Consequently, this result showed the possibility that the parameters could also be applied to spontaneous vocal emotion recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.328-330
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• 2007

Emotion is getting one of the important elements of the intelligent service robots. Emotional communication can make more comfortable relation between humans and robots. We developed emotional head robot system using 3D character. We designed emotional engine for making emotion of the robot. The results of face recognition and hand recognition is used for the input data of emotional engine. 3D character expresses nine emotions and speaks about own emotional status. The head robot has memory of a degree of attraction. It can be chaIU!ed by input data. We tested the head robot and conform its functions.

• Cartoon and Animation Studies
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• s.40
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• pp.477-496
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• 2015

Emotional awareness is the image of a person is represented by different tendencies. Currently, the emotion computing to objectively evaluate the emotion recognition research is being actively studied. However, existing emotional computing research has many problems to run. First, the non-objective in emotion recognition if it is inaccurate. Second, the correlation between the emotion recognition is unclear points. So to test the regularity of image sensitivity to the need of the present study is to control emotions in the computing system. In addition, the screen number of the emotion recognized for the purpose of this study, applying the method of objective image emotional computing system and compared with a similar degree of emotion of the person. The key features of the image emotional computing system calculates the emotion recognized as numbered digital form. And to study the background of emotion computing is a key advantage of the effect of the James A. Russell for digitization of emotion (Core Affect). Pleasure emotions about the core axis (X axis) of pleasure and displeasure, tension (Y-axis) axis of tension and relaxation of emotion, emotion is applied to the computing research. Emotional axis with associated representative sensibility very happy, excited, elated, happy, contentment, calm, relaxing, quiet, tired, helpless, depressed, sad, angry, stress, anxiety, pieces 16 of tense emotional separated by a sensibility ComputingIt applies. Course of the present study is to use the color of the color key elements of the image computing formula sensitivity, brightness, and saturation applied to the sensitivity property elements. Property and calculating the rate sensitivity factors are applied to the importance weight, measured by free-level sensitivity score (X-axis) and the tension (Y-axis). Emotion won again expanded on the basis of emotion crossed point, and included a representative selection in Sensibility size of the top five ranking representative of the main emotion. In addition, measuring the emotional image of a person with 16 representative emotional score, and separated by a representative of the top five senses. Compare the main representative of the main representatives of Emotion and Sensibility people aware of the sensitivity of the results to verify the similarity degree computing emotion emotional emotions depending on the number of representative matches. The emotional similarity computing results represent the average concordance rate of major sensitivity was 51%, representing 2.5 sensibilities were consistent with the person's emotion recognition. Similar measures were the degree of emotion computing calculation and emotion recognition in this study who were given the objective criteria of the sensitivity calculation. Future research will need to be maintained weight room and the study of the emotional equation of a higher concordance rate improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.57-62
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• 2010

Recently in the mobile market, the communication technology which bases on the sense of sight, sound, and touch has been developed. However, human beings uses all five - vision, auditory, palatory, olfactory, and tactile - senses to communicate. Therefore, the current paper presents a technology which enables individuals to be aware of other people's emotions through a machinery device. This is achieved by the machine perceiving the tone of the voice, body temperature, pulse, and other biometric signals to recognize the emotion the dispatching individual is experiencing. Once the emotion is recognized, a scent is emitted to the receiving individual. A system which coordinates the emission of scent according to emotional changes is proposed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.171-175
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• 2012

Recently, the research on ICT (Information and Communication Technology), which cognizes and communicates human's emotion through information technology, is increasing. For instance, there are researches on phones and services that perceive users' emotions through detecting people's voices, facial emotions, and biometric data. In short, emotions which were used to be predicted only by humans are, now, predicted by digital equipment instead. Among many ICT researches, research on emotion recognition from face is fully expected as the most effective and natural human interface. This paper studies about sensitivity ICT and examines mechanism of facial expression recognition system as an example of sensitivity ICT.